Data Processing

Interactive Code

Throughout this document, hover over the numbered annotations to the right of code chunks to reveal detailed explanations and comments about the code. Where drop-down italicized text is present, expand by pressing on arrow to see code.

Data Importation

Data Sources

Procedure

if (!requireNamespace("pacman", quietly = TRUE)) {
  install.packages("pacman")
}

renv was unable to query available packages from the following repositories:
- # http://www.stats.ox.ac.uk/pub/RWin/bin/macosx/big-sur-x86_64/contrib/4.3 --------
error downloading 'http://www.stats.ox.ac.uk/pub/RWin/bin/macosx/big-sur-x86_64/contrib/4.3/PACKAGES.rds' [error code 22]
error downloading 'http://www.stats.ox.ac.uk/pub/RWin/bin/macosx/big-sur-x86_64/contrib/4.3/PACKAGES.gz' [error code 22]
error downloading 'http://www.stats.ox.ac.uk/pub/RWin/bin/macosx/big-sur-x86_64/contrib/4.3/PACKAGES' [error code 22]


# Downloading packages -------------------------------------------------------
- Downloading pacman from CRAN ...              OK [376.8 Kb in 0.87s]
- Downloading remotes from CRAN ...             OK [419.1 Kb in 0.65s]
Successfully downloaded 2 packages in 13 seconds.

The following package(s) will be installed:
- pacman  [0.5.1]
- remotes [2.5.0]
These packages will be installed into "~/Documents/Universiteit/University of Hawai'i/MSc Thesis/Thesis/RQ2/nomilo-fishpond-analysis/renv/library/R-4.3/x86_64-apple-darwin20".

# Installing packages --------------------------------------------------------
- Installing remotes ...                        OK [installed binary and cached in 0.55s]
- Installing pacman ...                         OK [installed binary and cached in 0.48s]
Successfully installed 2 packages in 1.1 seconds.

pacman::p_load(
  renv, 
  yaml, 
  rmarkdown, 
  rio, 
  tidyverse, 
  janitor, 
  lubridate, 
  fs,
  hms, 
  zoo, 
  corrplot, 
  kableExtra, 
  psych, 
  ggplot2, 
  shiny, 
  rsconnect, 
  packrat, 
  plotly, 
  htmlwidgets
  )

create_vector_file_paths <- function(directory_path) {
  # List all files in the given directory path
  files_to_import <- fs::dir_ls(path = directory_path)
  
  # Loop through the files and print each with an index
  for (i in seq_along(files_to_import)) {
    cat(i, "= ", files_to_import[i], "\n")
  }
  
  # Return the vector of file paths
  return(files_to_import)
}

files_to_import <- create_vector_file_paths("data/raw")

1 =  data/raw/2024-02-28_dfs.RData 
2 =  data/raw/2024-02-28_ksf-clam-growth.xlsx 
3 =  data/raw/2024-02-28_ksf-compiled-data.xlsx 
4 =  data/raw/2024-02-28_ksf-oyster-cylinder-growth.xlsx 
5 =  data/raw/2024-02-28_profile-data.xlsx 
6 =  data/raw/2024-02-28_water-samples.xlsx 
7 =  data/raw/2024-02-28_weather-data.xlsx 
8 =  data/raw/2024-03-01_dfs-no-profiles.RData 
9 =  data/raw/2024-03-01_dfs_no_profiles.RData 
10 =  data/raw/2024-03-04_dfs-no-profiles.RData 
11 =  data/raw/2024-03-08_dfs-no-profiles.RData 
12 =  data/raw/2024-03-18_dfs-no-profiles.RData 

@iteratively-import-raw-data Code Chunk Execution Warning

The @iteratively-import-raw-data code chunk should only be ran once when raw data is updated because it takes long to execute. Therefore, run the @efficiently-load-raw-data code chunk instead to easily import up-to-date raw data.

dfs_no_profiles <- map(files_to_import[c(2:7)], import_list)
current_date <- format(Sys.Date(), "%Y-%m-%d")
save(dfs_no_profiles, file = paste0("data/raw/", current_date, "_dfs-no-profiles.RData"))

Ensure the Correct Index Value is Inputted Below

Refer to the output of the files_to_import data object to ensure you are inputting the correct index value corresponding to the file path that needs to be loaded.

base::load(files_to_import[10])

We will always use snakecase when naming our data objects and functions (e.g., data_object_name or function_name()).

names(dfs_no_profiles) <- gsub("data/raw/2024-02-28_|\\.xlsx$|\\.xls$", "", 
                               files_to_import[c(2:4, 6, 7)])
names(dfs_no_profiles) <- gsub("-", "_", names(dfs_no_profiles))

1

Remove prefixes and file extensions

2

Replace hyphens with underscores

dfs_no_profiles <- map(dfs_no_profiles, ~ set_names(.x, gsub(" ", "_", tolower(names(.x)))))

ksf_clams_growth_data <- dfs_no_profiles$ksf_clam_growth$sheet1
ksf_compiled_data <- dfs_no_profiles$ksf_compiled_data$full_data
ksf_oyster_cylinder_growth_data <- dfs_no_profiles$ksf_oyster_cylinder_growth$sheet1
water_samples_data <- dfs_no_profiles$water_samples$data_overview
weather_data <- dfs_no_profiles$weather_data$weather_ksf
tidal_data <- dfs_no_profiles$ksf_compiled_data$tides

sheets_to_import <- c("L1", "L2", "L3", "L4")

profiles_data <- profiles_data <- map_dfr(sheets_to_import, function(sheet_name) {
  import(files_to_import[5], which = sheet_name)
}) %>%
  bind_rows()

1

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2

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View Raw Data

ksf_clams_growth_data

ksf_compiled_data

ksf_oyster_cylinder_growth_data

water_samples_data

weather_data

profiles_data

tidal_data

Data Cleaning and Preparation

Tidying Raw Datasets

ksf_clams_growth_data_tidied

new_clam_var_names <- c(
  "sort_date", "color", "clams_in_count", "clams_in_lbs",  "clams_in_avg_per_lb",
  "clams_out_count", "clams_out_lbs", "clams_out_avg_per_lb", "growth_in_lbs", 
  "growth_pct", "sr", "days_btwn_sort"
  )

new_clam_date_col <- c(
  "2023-10-17", "2023-12-06", "2023-12-12", "2024-01-02",  "2024-01-10", "2024-01-24",
  "2024-01-31", "2024-02-08", "2024-02-13", "2024-03-14", "2024-03-19", "2024-03-27"
  )

ksf_clams_growth_data_tidied <- ksf_clams_growth_data %>%
  slice(-1) %>%
  setNames(new_clam_var_names) %>%
  mutate(date = as.Date(new_clam_date_col)) %>%
  dplyr::select(-sort_date) %>%
   pivot_longer(
    cols = c(
      clams_in_count, clams_in_lbs, clams_in_avg_per_lb,   clams_out_count, 
      clams_out_lbs, clams_out_avg_per_lb
      ),
    names_to = c("stage", ".value"),
    names_prefix = "clams_",
    names_sep = "_",
    values_to = "value"
  ) %>%
  mutate(stage = if_else(str_detect(stage, "in"), "In", "Out")) %>%
  rename(avg_per_lbs = avg) %>%
  mutate(across(c(color, stage), as.factor)) %>%
  mutate(across(c(count, lbs, avg_per_lbs, growth_in_lbs, growth_pct, sr),
                ~as.numeric(gsub("%", "", .)))) %>%
  arrange(date, color, stage) %>%
  dplyr::select(date, days_btwn_sort, color, stage, count, lbs, avg_per_lbs,
                growth_in_lbs, growth_pct, sr) %>%
  rename("days_btwn_clams_sort" = days_btwn_sort,
         "clams_color" = color,
         "clams_stage" = stage,
         "clams_count" = count,
         "weight" = lbs,
         "avg_weight" = avg_per_lbs,
         "clams_growth" = growth_in_lbs,
         "clams_sr" = sr, 
          "date_out" = date
         ) %>% 
    mutate(date_in = date_out - days_btwn_clams_sort) %>%
relocate(c(date_in, date_out), .before = days_btwn_clams_sort) %>% 
  mutate(
    date = case_when(
      clams_stage == 'In' ~ date_in,
      clams_stage == 'Out' ~ date_out
    )
  ) %>%
  select(-date_in, -date_out) %>% 
   mutate(grouping_variable = rep(1:(n() / 2), each = 2))


paged_table(ksf_clams_growth_data_tidied)

1

Manually set variable names

2

Assign dates to new date column

3

Delete first row

4

Set date as correct variable type and pivot data set based on date range.

5

Assign In and Out to stage

6

Rename variable of average to average per lbs

7

Set stage and color as factor variable types

8

Set variables as numeric variable types

9

Arrange values by date, color, and stage

ksf_compiled_data_tidied

ksf_compiled_data_tidied <- ksf_compiled_data %>% 
  rename_with(~gsub("\\s*\\([^\\)]+\\)", "", .x)) %>%
  janitor::clean_names() %>%
  rename(date = date_time) %>%
  mutate(date = as.Date(date)) %>%
  filter(date >= as.Date("2023-11-20") & date <= as.Date("2024-02-20")) %>%
  arrange(date) %>%
  dplyr::select(-c(external_voltage, wk_num, wind_dir,
                   spadd, outdoor_temperature, hourly_rain,
                   solar_radiation, resistivity, battery_capacity,
                   hour, daynum, data_pt, wind_sp, diradd,
                   wind_speed, wind_direction, tide, day, month, year)
                ) %>%
  dplyr::select(where(~ !anyNA(.))) %>%
  group_by(date) %>%
  summarise(across(where(is.numeric), \(x) mean(x, na.rm = TRUE))) %>%
  rename("ksf_salinity" = salinity,
         "ksf_rdo_saturation" = rdo_saturation,
         "ksf_rdo_concentration" = rdo_concentration,
         "ksf_actual_conductivity" = actual_conductivity,
         "ksf_total_dissolved_solids" = total_dissolved_solids,
         "ksf_ammonium" = ammonium,
         "ksf_barometric_pressure" = barometric_pressure,
         "ksf_oxygen_partial_pressure" = oxygen_partial_pressure,
         "ksf_specific_conductivity" = specific_conductivity,
         "ksf_density" = density,
         "ksf_chlorophyll_a_fluorescence" = chlorophyll_a_fluorescence,
         "ksf_ammonium_m_v" = ammonium_m_v)

paged_table(ksf_compiled_data_tidied)

1

Clean variable names by removing everything in parentheses, using lowercase and underscores in place of spaces

2

Rename the date_time variable to date, filter to desired date range and sort by date

3

Remove unnecessary variables

4

Remove columns with containing all NA values

5

Group by date and calculate the average of every variable for each day

ksf_oyster_cylinder_growth_data_tidied

oyster_var_names <- c(
  "date", "oyster_large_weight", "oyster_large_gain", "oyster_small_weight",
  "oyster_small_gain", "oyster_chlorophyll"
  )

ksf_oyster_cylinder_growth_data_tidied <- ksf_oyster_cylinder_growth_data %>% 
  dplyr::select(c(1, 4, 5, 8, 9, 12)) %>%
  slice(-1) %>%
  setNames(oyster_var_names) %>%
  pivot_longer(
    cols = c(oyster_large_weight, oyster_large_gain,
             oyster_small_gain,
             oyster_small_weight),
    names_to = c("oyster_size", ".value"),
    names_prefix = "oyster_",
    names_sep = "_",
    values_to = "value"
  ) %>%
  mutate(oyster_size = if_else(str_detect(oyster_size, "small"), "Small", "Large")) %>%
  mutate(date = as.Date(date),
         oyster_size = as.factor(oyster_size),
         across(c(weight, gain), as.numeric)
        ) %>%
  filter(date >= as.Date("2023-11-20") & date <=
           as.Date("2024-02-20")) %>%
  mutate(weight = weight * 0.00220462) %>% 
  rename("growth_pct" = gain) %>%
  select(-growth_pct) %>% 
  filter(!is.na(weight) & oyster_size != "Large")

paged_table(ksf_oyster_cylinder_growth_data_tidied)

1

Manually set variable names

2

Select desired columns and remove first row

3

Convert from wide to long format

4

Create a new variable that differentiates oyster size

5

Adjust data types to numeric and factor

6

Filter to desired date range

water_samples_data_tidied

water_samples_data_tidied <- water_samples_data %>%
  slice(-c(44:52)) %>% 
  rename_with(~gsub("\\s*\\([^\\)]+\\)", "", .x)) %>%
  janitor::clean_names() %>%
  mutate(
    date = if_else(date == "44074",
            as.character(as.Date("2024-01-09")),
            format(dmy(date), "%Y-%m-%d"))
  ) %>%
  mutate(sample_id = 1:nrow(.)) %>%
  mutate(date = as.Date(date),
         across(c(nomilo_id, location, round, depth), as.factor)) %>%
  select(-c(sample_id, nomilo_id, tube_name)) %>% 
  mutate(location = str_to_title(location)) %>%
  mutate(location = factor(location, 
                           levels = c("Mid Buoy", "Back Buoy", "Auwei", "Production Dock"))) %>% 
   filter(!is.na(location))

paged_table(water_samples_data_tidied)

1

Clean variable names by removing everything in parentheses, using lowercase and underscores in place of spaces

2

Replaces incorrect date values and format as YYYY-MM-DD

3

Add values for sample ID

4

Set correct variable types

weather_data_tidied

weather_data_tidied <- weather_data %>% 

janitor::clean_names() %>%
   unite(date, year, month, day, sep = "-") %>%
  mutate(date = ymd(date)) %>%
   select(-c(1, 3)) %>%
  rename("outdoor_temperature" = outdoor_temp_f) %>%
   mutate(outdoor_temperature = (outdoor_temperature - 32) * (5/9)) %>%
  group_by(date) %>%
  summarise(across(where(is.numeric), \(x) mean(x, na.rm = TRUE))) %>%
  slice(-1)

paged_table(weather_data_tidied)

1

Clean variable names

2

Merge separate day, month, year columns into one column variable and format as YYYY-MM-DD.

3

Cut columns

4

Rename outdoor temperature and convert from Fahrenheit to Celcius

5

Group by date and then take average values per day

6

Cut first row

profiles_data_tidied

new_profile_var_names <- c("depth", "water_temperature", "dissolved_oxygen", "salinity", "conductivity", "visibility", "location", "date")

profiles_data_tidied <- profiles_data %>%
  select(-c(6, 8)) %>%
  mutate(
    temp_column1 = NA_character_,
    temp_column2 = NA_character_
  ) %>%
  setNames(new_profile_var_names) %>%
  mutate(
    location = ifelse(depth == "Location", water_temperature, NA_character_), 
    date = ifelse(depth == "Date",  water_temperature, NA_character_)
  ) %>%
  fill(location, date, .direction = "down") %>%
  filter(depth != "Location", depth != "Date") %>%
  mutate(
    location = case_when(
      location == "L1 Northwest buoy" ~ "back buoy",
      location == "L2 Middle Buoy" ~ "mid buoy",
      location == "L3 Production Dock" ~ "production dock",
      location == "L4 Auwai" ~ "auwei",
      TRUE ~ location
    ),
    date = case_when(
      date %in% c("45258", "2023-11-28") ~ "2023-11-28",
      date %in% c("45282", "2023-12-21") ~ "2023-12-21",
      date %in% c("45536", "2024-01-09") ~ "2024-01-09",
      date %in% c("30/1/24", "30/01/24") ~ "2024-01-30",
      date %in% c("20/02/24", "20/2/24") ~ "2024-02-20",
      TRUE ~ date
    )) %>%
  mutate(
    date = as.Date(date, format = "%Y-%m-%d"),
     conductivity = case_when(
      row_number() %in% c(1:11, 53:62, 128:133, 159:161) ~ NA_character_,
      TRUE ~ as.character(conductivity)
    )
  ) %>%
  filter(!(depth %in% c("Samples", "Depth"))) %>%
  mutate(date = as.Date(date),
         across(c(depth, location), as.factor),
         across(c(water_temperature,  dissolved_oxygen, salinity, 
                  conductivity,visibility),  as.numeric)) %>%
   fill(visibility, .direction = "down") %>%
  mutate(visibility = if_else(date == "2023-11-28",  NA_real_, visibility)) %>%
  mutate(location = str_to_title(location)) %>%
  mutate(location = factor(location, 
                           levels = c("Mid Buoy", "Back Buoy", "Auwei", "Production Dock"))) %>% 
  mutate(conductivity = coalesce(conductivity, salinity)) %>% 
  rename("salinity1" = salinity,
         "salinity" = conductivity) %>% 
  select(-salinity1)

paged_table(profiles_data_tidied)

1

Set new variable names manually

2

Delete unnecessary columns

3

Temporarily create two new columns to replace the ones we deleted

4

Assign new profile variable names to rename variables in data set

5

Takes location from one column of location and date data, and assigns it to corresponding data of another column.

6

Fill values of temperature downwards in newly created date and location column.

7

Gets rid of the ‘location’ and ‘date’ rows that had empty values.

8

Renames values

9

Removes turbidity data rows 1:11

10

Looks for rows containing samples and depth names and negate these values.

11

Sets correct data types for each variable

12

Fills values from the temperature downwards into the newly created columns for date and location

tidal_data_tidied

tidal_data_tidied <- tidal_data %>% 

janitor::clean_names() %>%
  mutate(date = as.Date(date, format = "%Y-%m-%d"))  %>%
   filter(date >= as.Date("2023-11-20") & date <=  as.Date("2024-02-20")) %>%
  select(-2) %>%
  mutate(time = as_hms(format(time, "%H:%M:%S")),
         high_low = as.factor(high_low))
  
paged_table(tidal_data_tidied)

1

Clean variable names

2

Set date as correct variable type and format YYYY-MM-DD

3

Filter to desired date range

4

Cut column

5

Set time as time variable type

6

Set variable as factor type

Data Exportation

Exporting Processed Data

source("code/functions/export_to_csv.R")

dfs_to_export <- list(
  ksf_clams_growth_data_tidied = ksf_clams_growth_data_tidied,
  # ksf_compiled_data_tidied = ksf_compiled_data_tidied,
  ksf_oyster_cylinder_growth_data_tidied = ksf_oyster_cylinder_growth_data_tidied
  # water_samples_data_tidied = water_samples_data_tidied,
  # profiles_data_tidied = profiles_data_tidied,
  # biogeochem_vars_merged = biogeochem_vars_merged,
  # profiles_water_samples_merged = profiles_water_samples_merged,
  # tidal_data_tidied = tidal_data_tidied,
  # weather_data_tidied = weather_data_tidied
  )

imap(dfs_to_export, ~ export_to_csv(.x, .y, "data/tidied"))

1

List of dataframes we want to export as CSV files

2

Iterate the export_to_csv(df, df_name, dir_path) function over each dataframe. .x refers to the dataframe. .y refers to the name of the dataframe. These are passed to export_to_csv() function along with the desired directory path.

export_to_csv(profiles_data_tidied,
              "profiles_data_tidied",
              "data/tidied")